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Transformation towards a Smart Maintenance Factory: The Case of a Vessel Maintenance Depot


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The conceptualization and framework of smart factories have been intensively studied in previous studies, and the extension to various business areas has been suggested as a future research direction. This paper proposes a method for extending the smart factory concept in the ship building phase to the ship servicing phase through actual examples. In order to expand the study, we identified the differences between manufacturing and maintenance. We proposed a smart transformation procedure, framework, and architecture of a smart maintenance factory. The transformation was a large-scale operation for the entire factory beyond simply applying a single process or specific technology. The transformations were presented through a vessel maintenance depot case and the effects of improvements were discussed.
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Machines 2021, 9, 267.
Transformation towards a Smart Maintenance Factory: The
Case of a Vessel Maintenance Depot
Gwang Seok Kim * and Young Hoon Lee
Department of Industrial Engineering, Yonsei University 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea;
* Correspondence:
Abstract: The conceptualization and framework of smart factories have been intensively studied in
previous studies, and the extension to various business areas has been suggested as a future research
direction. This paper proposes a method for extending the smart factory concept in the ship building
phase to the ship servicing phase through actual examples. In order to expand the study, we iden-
tified the differences between manufacturing and maintenance. We proposed a smart transfor-
mation procedure, framework, and architecture of a smart maintenance factory. The transformation
was a large-scale operation for the entire factory beyond simply applying a single process or specific
technology. The transformations were presented through a vessel maintenance depot case and the
effects of improvements were discussed.
Keywords: smart factory; smart process transformation framework; smart maintenance
architecture; smart maintenance factory
1. Introduction
A smart factory is a production plant where the pillars of Industries 4.0 are imple-
mented, including additive manufacturing (3D printing), augmented reality (AR), Inter-
net of Things (IoT), big data analytics, autonomous robot, simulation, cyber-security, ver-
tical and horizontal integration, and cloud computing [1]. The concept of a smart factory
has become a keyword of manufacturing sites along with the technological development
during the fourth industrial revolution.
Hyundai Heavy Industries, Daewoo Shipbuilding & Marine Engineering Co., Ltd.,
and Samsung Heavy Industries are the three major global ship manufacturers in Korea
that lead smart ship and yard constructions by applying the new technologies of the
fourth industrial revolution and utilizing IT systems [2]. In the report by SPAR Associates.
Inc., which have been serving in the shipbuilding and repairing industry for over 45 years,
about 23% of the total cost is ship acquisition cost, approximately 35% is labor cost, and
the remaining 42% is repairing and maintenance in the Naval Ship Life Cycle Cost (LCC)
Model [3]. Although the growth of a smart factory is limited to the ship building phase,
shipbuilders are interested in further expanding the smart factory concept to the service
phase in order to expand the scope of the maintenance, repair, and operations (MRO)
business. It is necessary to expand the research on manufacturing-oriented smart factory
research to cover the entire lifecycle.
This study takes focus on a practical case that expands the entire ship lifecycle from
build phase to service phase, as shown in Figure 1.
Kim, G.S.; Lee, Y.H.
Transformation toward Smart
Maintenance Factory: Case of a
Vessel Maintenance Depot.
, 9, 267.
Academic Editor
: Zhuming Bi
Received: 31 August 2021
Accepted: 30 October 2021
2 November 2021
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Machines 2021, 9, 267 2 of 20
Figure 1. Ship lifecycle and smart factory expansion.
The adaptability of the concept of a smart factory to a repair and maintenance factory
(hereafter referred to as a maintenance factory) requires an understanding of the differ-
ences at work. Ship building is the process of assembling modules produced in factories
and yards in accordance with the job schedule, whereas repair and maintenance tasks
usually do not follow a fixed assembly process plan. Repair and maintenance work has
the following variabilities:
Variability in the plan: Ship building is progressed step by step following the work-
ing plan, and the supply of each relevant component is proceeded on a planned basis. In
contrast, unplanned repair and maintenance requirements frequently occur since failures
of ships are unpredictable. Moreover, repair and maintenance plans may vary due to the
dock’s idle state because ships must be drawn on the dry dock.
Variability in the process: The process of ship building is usually done at fixed work-
places and workstations. However, the process of repair and maintenance include: draw
the ship to the dry docktake away parts from the shiptaking parts out from the ship
moving to the factorydisassembling partscleaningrepairingreassemblingtest-
ing performancemoving from the factory to the shiptaking in to the shipassembling
on the shiptrial run. The required jobs in the repair and maintenance process to re-
pair/replace defective parts and improve performance are different from jobs in the ship
building process.
Variability in the time: Planned demand and standard working hours are set for pro-
duction and manufacturing. However, the work time of repair and maintenance varies
depending on the degree of failure and repair requirements. Because the delivery date of
each part is different (e.g., discontinued parts or parts that have a long delivery period),
repair and maintenance are hard to complete in a timely manner.
Variability in the workplace: In the case of ship manufacturing, the parts are first
assembled in the factory and then at the yard. On the other hand, repair and maintenance
are not only proceeded in the factory; the process may be done on the ship or through
remote maintenance system as needed.
The purpose of this paper is to present a method to extend the smart factory concept
of the build phase to the service phase through empirical examples. Smart transformation
procedure, the framework, and architecture were developed by repeating revisions and
improvements in the process of establishing a smart transformation plan for about one
The four contributions of this study are as follows. First, this study provides the trans-
formation procedure for a smart maintenance factory. Second, this study developed a
smart process transformation framework for building a smart maintenance factory to im-
prove highly volatile processes. Smart transformation occurs when site workers under-
stand strategies and voluntarily draw actual changes. To this end, this study developed a
practical template for site workers to participate in change and present their opinions.
Third, this study proposed the architecture of a smart maintenance factory that shows the
future look of a smart maintenance factory. This study suggested the value and technol-
ogy for transforming an existing factory to a smart repair and maintenance factory while
giving consideration to the characteristics of the process. Finally, smart transformation
Machines 2021, 9, 267 3 of 20
was implemented for the vessel maintenance depot. Transformation is a large-scale oper-
ation that changes the entire factory beyond simply applying a single process or specific
The remainder of this paper is composed as follows. Section 2 reviews previous stud-
ies, Section 3 introduces the materials and methods, Section 4 presents the application
cases, and in Section 5, we discuss the results, address the conclusions, and suggest direc-
tions for further research.
2. Literature Review
This study reviewed previous studies and categorized them into a practical applica-
tion and research extension from ship building phase to ship servicing phase. Research of
the building phase could be divided into smart manufacturing for the production process,
smart management by information, and smart maintenance for 5M + 1E (Man, Machine,
Material, Method, Measurement, and Environment), which supports production, as
shown Figure 2.
Figure 2. Research on smart factory and expansion.
Among numerous studies conducted in each area, the concept, the recent direction,
and research on the implementation will mainly be analyzed.
Smart manufacturing is a term coined by several agencies such as the Department of
Energy (DoE) and the National Institute of Standards and Technology (NIST) in the
United States. It highlights the use of information and communication technology (ICT)
and advanced data analytics to improve manufacturing operations on the shop floor [4,5],
factory [6], and supply chain [7,8].
Smart manufacturing incorporates various technologies, including cyber-physical
production systems (CPPS), IoT, robotics/automation, big data analytics, and cloud com-
puting to realize a data-driven, connected supply network [9]. Intelligent manufacturing
has often been used synonymously with smart manufacturing. Technologies and enabling
factors associated with smart manufacturing were reviewed to compare the differences
between smart manufacturing and intelligent manufacturing [4,10,11]. Studies on the fea-
tures and availability of smart manufacturing and intelligent related technologies were
carried out [12]. Compared to smart manufacturing, intelligent manufacturing focuses
more on the technological aspect and less on the organizational aspect.
The opportunities and organizational issues to be considered during smart transfor-
mation were analyzed for SMEs [13]. A smart manufacturing performance measurement
Machines 2021, 9, 267 4 of 20
system was introduced based on exploratory and empirical research [14]. According to
these studies, a guide for investment in smart manufacturing was presented to ensure the
validity of investment. This study used a top-down approach to introduce the specific
implementation scheme from top-level planning for industrial practice in smart manufac-
turing [15].
By referring to the studies of smart maintenance in the build phase, the applicability
and implications for the service phase were examined. We analyzed the latest research on
a smart maintenance concept, the changing trend of the existing IT system, recent inten-
sive predictive maintenance studies, and maintenance architecture. Smart maintenance
was defined as based on four main components through expert interviews and prelimi-
nary research on manufacturing plants. The four main elements are data-driven decision-
making, human capital resources, internal integration, and external integration [16]
The Enterprise Resource Planning (ERP) system, which manages materials, budget,
and planning, and the Manufacturing Execution System (MES), which manages field data,
are the two representative factory management systems to support production. When a
traditional factory is transformed into a smart factory, the existing ERP and MES systems
would be advanced by applying cloud technology, big data-based data diagnostic, and AI
technology. The existing information management systems of smart factory processes re-
quire more flexibility and a larger volume of data, which needs to connect to related sys-
tems, to analyze, and to visualize the activities inside the organization [14,17,18]. The in-
troduction of AI systems that support decision making by making knowledge of the pro-
duction process between each system and data collected from various smart devices is
spreading [15,19].
In addition to academic research, the technology trends suggested by the operators
in the maintenance industry were analyzed and reflected in this study. The five transfor-
mational Trends Reshaping Industrial Maintenance [20] are: (1) Additive (3D printing)
Manufacturing in Maintenance, U.S. Department of Transportation to publish a notice
which aims at raising awareness about the use of Additive Manufacturing in the mainte-
nance, and preventive maintenance areas; (2) Internet of Things, Wireless Sensor Net-
works, and IoT-based automated data collection increases workers’ productivity; (3) Aug-
mented Reality (AR) for Training and Remote Maintenance. AR is enabling new para-
digms for maintenance, including remote maintenance and maintenance customized to
the workers’ under-standing and skills; (4) Maintenance as a Service (MaaS) could become
a game changer in industrial maintenance. It can motivate machine vendors to provide
the best service while also providing versatile, reliable, and functional equipment; (5) Sup-
ply Chain Collaboration. Streamlining the supply chain management information has
been proven to be extremely beneficial for industrial maintenance as well, as it reduces
the delivery times for parts. At the same time, supply chain operators benefit from mainte-
nance insights, such as predictive maintenance.
The other operator of Manufacturer [21] stated that ERP and MES should be data-
driven management solutions to support Industry 4.0 as follows: (1) Modern ERP systems
must be built fundamentally different from the ground up; (2) The ERP system should
also be architected to interact with external systems with application programming inter-
faces (APIs) available for any and all entities of the system; (3) IoT should be able to make
almost any product a smart, connected product. IoT operations support traditional MES
for shop floor automation and control adding the flexible communication and data collec-
tion protocols. The research theme of smart maintenance is changing from Time Based
Management (TBM) to Condition-Based Maintenance (CBM) [22] for predictive mainte-
nance and research on optimizing preventive intervention through CBM [23]. Studies on
collaborative-based architecture [24], web platform [25], decision support [26], sensor and
data analysis for predictive maintenance were discussed for CBM [27].
A study on the basics for designing the maintenance process of Industry 4.0 [28] and
a study on performance and KPI design are also presented [29]. SMEs do not exploit all
the resources for implementing Industry 4.0 and often limit themselves to the adoption of
Machines 2021, 9, 267 5 of 20
Cloud Computing and the IoT [30,31]. These limitations will be experienced not only in
this study but also in most organizations.
In order to expand the smart factory concept to the service phase, practical applica-
tion research, and applications were emphasized [6,7,32]. The difficulties in applying the
concept of a production-oriented smart factory to a smart factory in the maintenance area
were discussed by site workers. The staff of vessel maintenance depot stated the limita-
tions in applying smart factory to the field of repair and maintenance sector. “Despite the
rapid growth of technology due to the fourth industrial revolution, smart factories are
applied in the maintenance field, not the manufacturing field, but in reality, there are tech-
nical limitations. Since nobody has ever done it, no one has been able to strongly suggest
to what extent it should be specified or what is needed to realize the future of the smart
vessel maintenance depot. [33], (p30).”
We reviewed the smart factories for the ship building phase, including the smart
manufacturing, management, and maintenance of a smart factory and the need for re-
search expansion in the service phase. The previous studies on the procedure, modeling,
and architecture to build a smart maintenance factory were also reviewed.
Reference [34] presented a step-by-step methodology for the efficient planning of a
smart factory from the initial idea to the final realization in the real environment. The
construction process from design, smart machining, smart monitoring, smart control,
smart scheduling, industrial applications, and the phases of data utilization were intro-
duced [35]. The importance of stepwise starting from a small scale were argued [36,37].
The new technology application in a limited area were demonstrated by building a smart
factory [38]
Existing studies introduced different models for the smart factory model. Reference
[39] proposed a human-centered model, [40] suggested IoT-based, [41] proposed IoT and
cloud computing, and [35] proposed a cloud-based control system as smart control sys-
tems. Operation values are defined based on the environment and work of the mainte-
nance factory. Reference [42] provided an overview of these principles in terms of the
general scope of Industry 4.0, and [7] investigated and analyzed the principles of a smart
factory and proposed modularity, interoperability, decentralization, virtualization, ser-
vice orientation, and real-time capability. Reference [36] suggested features of connectiv-
ity, optimization, transparency, proactivity, and agility for smart factory construction.
Different studies on smart factory architecture were presented. Reference [24] intro-
duced a data-based smart maintenance architecture and reference model to perform pre-
dictive equipment maintenance in a factory. Reference [43] described the three major cri-
teria of the general system architecture, including mechatronic changeability, individual-
ized mass production, and internal/external networking. Product, production layer, sup-
ply layer, integration layer, and IT are defined as the five layers. Reference [44] empha-
sized the integration of industrialization and informatization as the core of China’s smart
manufacturing implementation strategy and proposed the standards framework and ref-
erence architecture of a smart factory. Reference [45] presented a technical architecture
and argued that the interoperability of the systems or components of the architecture at
every level is imminent. References [46–48] proposed the hierarchical architecture of a
smart factory including four layers, namely the physical resource layer, network layer,
data application layer, and terminal layer.
3. Materials and Methods
As suggested in previous studies, starting on a small scale [36,37] or limited area [38]
seems to be an effective way to minimize risk, but there may be situations in which it is
stopped or scaled down due to various obstacles in the process. Procedures for smart con-
version of specific tasks are limited in their application of transforming the entire plant.
Few studies suggested a method for the procedure for building a smart maintenance fac-
tory in the field. We developed a hybrid procedure that combines the top-down procedure
that presents the future image and goals of a smart maintenance factory and the bottom-
Machines 2021, 9, 267 6 of 20
up procedure that reflects on the site conditions and requirements for changes. After ap-
proximately one year of revision and improvement, this procedure was eventually devel-
oped as shown in Figure 3
Figure 3. Implementation procedure.
(1) Goal and Scope
It is desirable to set the goal of a smart maintenance factory based on the organiza-
tions missions or policies, instead of the goal of an individual task or technology. In gen-
eral, the primary goal of maintenance factory management is to minimize variability,
which is a characteristic of maintenance, in order to complete the maintenance on time.
The safety of workers, jobs on the site, and the quality of maintenance could be ensured.
Another important goal is to increase the availability of ships. As a large-scale investment,
increasing availability is compared to increasing the value of investment.
(2) Requirements of Shop Floor
The requirements of transforming each workplace of the maintenance factory to a
smart factory are analyzed for selecting tasks to be improved. Such transformation re-
quirements are handled systematically based on the smart process transformation frame-
work developed. The characteristic of the smart process transformation framework pre-
sented in this study is that it leads to changes in the repair and maintenance process
through the following three elements of smart transformation: data, system, and automa-
tion (technology).
While the traditional approach derived the necessary elements of data, system, and
automation through the direction of process change, the smart process transformation
framework in this study intends to lead process change through actual technologies of the
fourth industrial revolution. In addition, based on long-term field analysis and opinions
from site workers, this study identified that data, IT systems, and automation (technology)
take the lead in major changes in smart maintenance plants and thus reflected them in the
Machines 2021, 9, 267 7 of 20
As illustrated in Figure 4, the smart process transformation framework is composed
of data, systems, and automation (technology) that induce process change. The smart
transformation strategy should be defined with KPI and the infrastructure and communi-
cation network should be selected to support the smart transformation. The organiza-
tional capabilities required for the smart transformation operation should also be planned.
Figure 4. Smart process transformation framework.
Ongoing effort and time are required to obtain the improvement needed from field
managers through the smart process transformation framework. Getting new opinions is
not easy since most workers and managers are immersed in existing work methods. Thus,
it is necessary to provide a specific and convenient template that shows the transformation
direction to figure out the challenges to change the viewpoints of site workers and man-
agers. The template effectively helps derive the requirements of the site and encourage
participation of the working site. Figure 5 illustrates the template for concretization of
changes on the site, which reflects the elements of the smart process transformation frame-
Figure 5. Template for analysis of on-site requirements.
The template consists of the working process on the horizontal axis and the smart
transformation framework shown in Figure 4 on the vertical axis. The requirements for
jobs constituting the process are classified and organized into automation, IT system, data,
Machines 2021, 9, 267 8 of 20
operating infrastructure and communication network, and strategy. Automation de-
scribes smart technologies and equipment needed for smart transformation. In the IT sys-
tem, the requirements and functions of the operation support system that support the job
are described. Data describes the required data set and format. Support infrastructure re-
quirements such as warehouse and power system are described as infrastructure, and re-
quirements for sensors and communication networks are described in network. Strategies
are described in connection with improvement effects in each job and KPIs. In addition,
deletion, and integration of jobs in the process are also described.
Workers on the site are sensitive to organizational changes and may consider the
transformation towards a smart maintenance factory a negative effect on job stability [30].
It is a reaction that can be easily felt in the field. Thus, it is recommended not to ask work-
ers on the site for opinions on changes in organization and manpower.
(3) Building Smart Maintenance Model
The model of a smart maintenance factory is according to the requirements on
changes to be made in strategic direction and operational values. Studies have introduced
different models such as a human-centered model [39], IoT-based model [40], and cloud
computing [35,41] for a smart factory. Principles were suggested, such as modularity, in-
teroperability, decentralization, virtualization, service orientation, real-time capability,
connectivity, optimization, transparency, proactivity, and agility for smart factory con-
struction [7,36].
The smart maintenance factory model consists of missions, a target model, opera-
tional values, and the technology enabling the technologies of the fourth industry revolu-
tion for process transformation. The target model of a smart maintenance factory can be
defined in various shapes by integrating the as-is state and strategic direction. When de-
fining the smart maintenance model, it is important to consider how to respond to varia-
bility, which is a characteristic of maintenance work. Agile response, predictive, and read-
iness should be defined as core operating values in order to respond to changes in the
work plan, process, time, and workplace of repair and maintenance work.
As an enabling technology for realizing operational values, it enables agile response
to changes on site through central control and mobile network. For predictive mainte-
nance, operational data of the vessel are collected and analyzed by a big data system. In
addition, inventory and work readiness are promoted through on-site operational sup-
port IT systems and smart warehouses. These operating values and required enablers can
be derived through on-site interviews.
(4) Define Tasks to Transform
Tasks define the activities required for smart factories to achieve high performance,
from policy, process improvement, data improvement, system construction, infrastruc-
ture improvement, and organizational change. Defining tasks is necessary in connection
with the workload, schedule, budget, and expected effects to be pursued in the future, so
an organizational consensus process is required through several meetings. After defining
the task, the selection of new technologies and targeting levels to implement the task
should be followed.
(5) Identify Smart Maintenance Elements
It is a step to define specific technological enablers to implement smart transfor-
mation with the selected tasks. Enablers consist of changes in maintenance policies, work
standards, and the selection of appropriate technologies. The technology enablers consist
of ICT systems for managing the working process and necessary 5M + 1E, automation,
data collection, data system for management and analysis, a network that connects data
to the entire maintenance factory, a central control system that manages it in real time,
and a smart factory infrastructure.
Selecting the right technologies is one of the main tasks for the smart maintenance
element. Reference [49] reviewed research on technology selection over the past 20 years
Machines 2021, 9, 267 9 of 20
to find out various new technologies needed for manufacturing and various methods for
technology selection due to the emergence of the fourth industrial revolution. In selecting
Industry 4.0 technology, it is recommended that long-term development or selection of
unstable technology be avoided. The hasty or wrong technology selection will weaken the
overall work productivity of the plant. It is recommended to apply validated and reliable
innovative technologies to the field at the initial stage, and to choose a business operator
that has sustainable development and maintenance capabilities. As an advanced work, a
written confirmation on the time and degree of the technology application should be sub-
mitted for applying a particular technology that is required.
The required technologies and the purpose of utilizing technologies for smart
maintenance are referred from smart manufacturing. Reference [50] insisted on end-to-
end ICT-based integration between the manufacturing technologies of smart machines,
warehousing systems, and production facilities that have developed digitally and feature
end-to-end ICT-based integration. Reference [34] mentioned equipment, a cloud-based
control system, communication network for real-time data collection and control, and the
importance of power monitoring to secure stable operation.
(6) Build Smart Maintenance Architecture
By configuring the architecture based on the operational values, the future shape of
the smart maintenance factory and its implementation technology are identified. The ar-
chitecture of a smart maintenance factory presents the direction of technical realization
for constructing the smart maintenance factory. The architecture may be presented differ-
ently by point-in-time, by factory, or by composition direction.
Studies have introduced the data-based smart maintenance architecture [24], the in-
ternal/external networking architecture [43], the integration of industrialization and in-
formatization architecture [44], the interoperability technical architecture [45], and the hi-
erarchical architecture of smart factory [46–48].
This study set one offsite layer and six onsite layers to classify the values and required
technological elements. The feature of the smart maintenance factory architecture in this
study contains seven layers according to the implementation characteristics, and mainte-
nance was divided into factory maintenance and remote maintenance of vessel according
to the required services and technologies. It also expressed the connection between busi-
ness management and field management as shown in Figure 6.
Figure 6. Architecture of a smart maintenance factory.
The first layer, Business O&M, is composed of ERP components that emphasize man-
agement efficiency. It is composed of systems that share information through the organi-
zation, such as scheduling, human resource, budgeting, procurement, etc. The other six
Machines 2021, 9, 267 10 of 20
layers are field-oriented functions. Operational values such as visibility, stability, and
speed should be set to guide the design direction of each layer.
The control, intelligence, data, and network layers transform the visualized and data-
driven maintenance factory through close interlocking. The control layer manages risks
and fluctuations in the schedule and environment through the central control screen. In
the intelligent layer, the field workers want to secure the process visibility of their jobs
through the operation support system. In the data layer, the maintenance process is iden-
tified by sharing specific data on-site. To this end, it consists of technical elements of data
standardization, collection, and sharing. For remote maintenance, the operational condi-
tions are monitored through remote diagnostic and operational supporting system.
In the automation layer, productivity and safety are focused. It is composed of indus-
try 4.0 technologies applicable to each workplace to secure safety and productivity. The
network layer emphasizes connectivity for collecting data and sharing information of
workers and materials through wireless and wire. The remote maintenance is connected
to the vessel via satellite.
The shop flow emphasizes speed, quality, and safety. The shop flow layer consists of
the actual work processes and operational infrastructures. For remote maintenance, the
vessel is considered as the shop floor.
(7) Analyze Effect with KPIs
KPIs and effect analysis are the high priority parts of executing Industry 4.0 technol-
ogies in tasks and securing budgets. Reference [51] pointed out the direction, the contents
of the role, and the importance of key performance indicators of application Industry 4.0
technologies. Reference [52] demonstrated the expected effect of building a smart factory
and analyzed the effect of smart factory adoption with an empirical analysis based on a
sample representing local manufacturing units.
It is important to define KPIs based on the missions and operational value of the
maintenance factory. Although it is appropriate to present KPIs and the improvements of
a single task if a single task-oriented process is applied, it is essential to select the expected
effects and KPIs for the maintenance factory if the entire factory is transformed step by
step, as presented in this study. Linking KPIs with the expected effects is an operation
optimization plan that is done after the establishment of a smart maintenance factory by
aligning strategy and implementation.
(8) Plan Implementation
There are two ways to construct a smart maintenance factory. The first way is to pri-
oritize technology development to evaluate performance and expected effects, and then
apply them to the workplace on a technical basis. It is to expand according to the technol-
ogy development stage. The other way is to select a specific workplace as a pilot, apply
all technologies that are under consideration, and then evaluate the performance and de-
ploy the technologies horizontally to other work processes. It is to expand by the organi-
zation or process unit.
While the first way has the disadvantage that it requires a long transformation time,
failure of technology may also cause failure of the smart transformation. The second way
is preferred if the smart maintenance factory needs to constructed in the short term, be-
cause members of the organization are guided to participate in the processes, and im-
provements or difficulties can be identified immediately in the field. Showing step-by-
step outputs will be an important factor that accelerates the implementation of smart
transformation to ensure enough budgets throughout the process. In the implementation
phase, the priority of transformation tasks will be determined according to budget size
and urgency.
4. Application
Machines 2021, 9, 267 11 of 20
The vessel maintenance depot was operated by the owner of a vessel established
about 70 years ago, and it is in the process of smart transformation from mechanical and
hydraulic maintenance to 3D printers, articulated lifts, and pilot-level remote mainte-
nance. Currently, the smart factory is evaluated as Level 2.0 of mechanization and com-
In the vessel maintenance depot, vessels are towed to the dry dock and the broken
parts are separated from the vessel to be repaired in the factory. Vessels are sent to the
maintenance depot for regular maintenance and emergency repair due to failure. This
study derived the requirements for building a smart maintenance factory from more than
20 maintenance processes. The field issues to be improved through smart maintenance
factory construction are shown in Table 1 below.
Table 1. Issues to be transformed in the vessel maintenance depot.
Classification Issues On Site Improvement Direction
Need to improve chronic delay in repair parts.
Predictive Maintenance
Need to improve poor linkage between schedules
Need fine-grained management of worker and working hours
Avoid concentration on maintenance work at a specific time
Neutralization of planned schedules by sudden maintenance
Need information system to share and collect data on site Data Diagnostic
Data-based analysis and managing on-site data are required for predictive maintenance
Need to identify maintenance history on the site
Maintenance knowledge should be secured
Onsite Operation
/ Monitoring
Insufficient support for the on-site maintenance process by the existing system
Integrated control and work monitoring system are needed on site
Real-time monitoring system is required for safety management
Need to check equipment status information for timely maintenance
Supporting of remote maintenance are increasing
Need automation for work safety and reducing work lord
(safety and productivity)
Need to improvement of workability, convenience, and safety
Insufficient quality control and deterioration of quality control ability
Technology required to perform precise tasks such as positioning a ship
Precise measurement system required for direct production and quality assurance of
manufactured products
Technology and equipment required to support remote maintenance
Need for mobile device, sensors, and network for data entry and automatic data collection in
the field IoT & Mobile
Emphasis on additional and emergency power supply system
Safety and Health
Warehouse needed for immediate supply of inventory
Removal of hazardous substances such as hazardous gas, waste oil, and dust during
Machines 2021, 9, 267 12 of 20
The transformation of a smart maintenance factory is to establish a predictive mainte-
nance process that is to respond to changes in demands and plans above all else. To this
end, the current task-oriented management would be transformed to data-oriented man-
agement. The central operation supporting system of the management level would be
transited to the on-site supporting system including the monitoring system. The automa-
tion was expected to reduce the burden of moving heavy parts and to improve work qual-
ity from poor precision. The entire maintenance depot would be connected and trans-
formed into a mobile workplace. A safe and healthy work environment was also required
in order to respond to environmental regulations. The research in this paper belongs to
the design stage, not the implementation stage, and the simulation of the expected result
is presented as a goal of improvement.
A diagnosis of the current factory at the smart factory level was conducted through
years of learning, and the transformation from the existing factory to a smart maintenance
factory was confirmed. Nonetheless, the managers of the vessel maintenance depot in-
tended to carry forward the smart transformation of the existing factory, but they are fac-
ing practical difficulties in planning to build the smart maintenance factory [33].
(1) Goal and Scope
This goal has been formulated as a unique mission of the maintenance depot. The
goal of the maintenance depot is to maintain and improve performance during the life
cycle of a vessel, and to improve the productivity of maintenance factory.
(2) Requirement of Shop floor
For each process, field workers were able to present automation, system, data, infra-
structure, and expected effects according to the provided template. In addition, some pro-
cesses were able to be integrate in response to changes. Figure 7 illustrated the practical
requirements that were derived by using templates of the smart process transformation
Figure 7. Example of requirements analysis for smart transformation.
(3) Build Smart Maintenance Model
The maintenance depot requests an on-site smart maintenance factory model that can
support and manage tasks at the site. The on-site smart maintenance factory is no longer
equipment-oriented. Instead, a data-and technology-centered construction direction was
born after repeated discussions and strategic presentations. With consideration of the re-
quirements and strategic direction of the derived smart transformation, the strategic
model for constructing a smart maintenance factory focuses on two directions.
First, the goals of a data-oriented smart maintenance factory include visualization of
the maintenance process and predictive maintenance. Second, the technology-oriented
Machines 2021, 9, 267 13 of 20
smart maintenance factory is to adopt Industry 4.0 technologies considering job specialty
of maintenance.
(4) Define Tasks to Transform
Tasks to build a smart maintenance factory of data driven and technology driven:
Task 1: data diagnostic management;
Task 2: on-site operation support system;
Task 3: semi-automation;
Task 4: remote maintenance system;
Task 5: IoT and mobile workplace;
Task 6: operational and environmental infrastructure.
The six tasks were determined in consideration of the analyzed issues in the field and
the required direction of resolution. In order to implement the six tasks, about a dozen
detailed tasks have been embodied. The tasks were finalized through collaboration and
considerable time and consultation with managers and the project team.
(5) Identify Smart Maintenance Elements
Regarding automation and cutting-edge technologies for the smart maintenance fac-
tory, Industry 4.0 technologies that suit the specificities of the tasks in a maintenance fac-
tory were decided to be applied. Among the various Industry 4.0 technologies, perfor-
mance-validated technologies and currently applicable technologies that do not require
long development and preparation time were prioritized.
In order to build a data diagnostic management of task 1, the data set must be defined
in advance. Since the existing IT system focuses on data storage and loss prevention, the
data management for utilization is insufficient. There is a lot of data stored in the system,
but usually the data is improper or the data format is incorrect for factual use. It is neces-
sary to establish governance rules for data standardization and collection in the field. The
big data system for analyzing a large amount of collected data and Quick Response (QR)
for sharing data in the fields are major systems for a data-driven smart factory. The QR
system was applied to allow verification of the maintenance history and objects at the site.
In order to implement task 2, the on-site support system is connected to the central
management system to share ERP information. It also plays roles of data collection, job
history identification, and job processing status on the site. The information of workers,
job status, job schedules, working inventories, and dangerous environments on the site
can be visually managed through the central control room with a huge screen. The control
room ensures comprehensive and rapid response to the various changes on the site.
The cloud-based smart control requires standardized maintenance works and proce-
dures. Standardization is difficult due to variability in the maintenance area. Thus, unlike
manufacturing, establishing a separate on-site support system and linking it with the cen-
tral information and data sharing system may be a method to construct smart mainte-
nance effectively in a short period of time.
For task 3, semi-automation is more preferable to full-automation considering the
changeable working conditions. Full automation is recommendable on the basis of a stable
work environment, planned work schedules, and repetitive work procedures. Neverthe-
less, it is not proper to apply full automation to a maintenance factory where works are
unstable and highly changeable.
The specific technology for semi-automation was selected in consideration of the
working conditions and technology availability. 3D scanning technology for accurately
locating a vessel and smart vehicle/cart technology for the convenient movement of work-
ers are selected. For smart moving between processes, smart vehicles were selected as the
optimal technology rather than Automated Guided Vehicle (AGV) due to environmental
variations, advanced works, and excessive costs. In order to replace discontinued parts,
3D scanning and printing technologies were applied to produce the parts internally with-
out design works. Co-robots are brought onto the site to assemble/disassemble heavy
Machines 2021, 9, 267 14 of 20
components that need to be repaired, promoting convenience of work. In addition, wear-
able equipment was introduced to reduce health and safety risks because workers no
longer need to manually move heavy weights frequently. CPS (Cyber-Physical Systems)
requires digitization of all landmarks of the factory, which takes a considerable amount
of time and expense. In this case, a lower-level or simple CPS technologies should be se-
lected. It is not necessary to apply the most advanced Industry 4.0 technologies to build a
smart maintenance factory.
In order to implement task 4, an AR system was implemented to support remote
maintenance. Workers and managers had a preconceived notion that VR technology
shown in the media could change the way of work. However, in order to apply VR tech-
nology, it could only be applied to preliminary preparation work and repetitive and lim-
ited work environments. In addition, there have been studies [5,15,26,53] on the limita-
tions of the application of AR technology to maintenance work in advance. Simple AR
technology that can give immediate instructions while sharing a maintenance site through
a remote screen was finally selected.
In order to implement task 5: the Internet of Things (IoT) and a mobile workplace, a
mobile network based on IoT and Long Term Evolution (LTE) was built as a communica-
tion network. The operation condition data of engines and the major equipment of vessels
is collected and transmitted through a satellite network in real-time. The data collected
from the vessel is diagnosed in the big data system for predictive maintenance. The col-
lected data are aggregated and operated on the IoT platforms and the on-site support sys-
tem, and are connected to the big data analysis system for predictive maintenance.
To implement task 6, operational and environmental infrastructure, facilities for haz-
ardous substances and the environment from maintenance were built as separate collec-
tive facilities. The Energy Saving System (ESS) is equipped for a stable power supply for
the increase in power consumption due to the smart maintenance factory.
(6) Build Smart Maintenance Architecture
Figure 8 illustrates the technical architecture for the smart maintenance factory.
Figure 8. Technology architecture of a vessel smart maintenance factory.
The operating system of a smart maintenance factory promoted the integrated logis-
tics support system of Business O&M (Operation and Management), for information con-
nectivity between the upper operating system and the on-site operating system. The cen-
tral control system consists of technologies similar to CCTV technology, such as big
screen, location identification, emergency call, remote conference, and messaging technol-
ogies. The operating support system (OSS) includes a factory management system and a
task management system to support works at the site. The data sets are identified and
Machines 2021, 9, 267 15 of 20
standardized in type and collection period considering usage from the site. The QR system
was applied extensively to most works and materials at the maintenance site. Automation
is mainly composed of Industry 4.0 technologies. In this case, semi-automation and ease
of movement were focused due the characteristics of the works.
Three types of networks were applied: near distance communication by using IoT,
long-range communication via LTE, and remote communication via satellite. The wireless
and sensor networks allowed workers to access information conveniently and assured
that data created at the field could be collected immediately. Changes that occurred in
each work process due to smart data and technology should be carried out in future con-
struction and implementation stages. Acquiring data and technical experts to operate
smart factories is another task to be prepared to increase organizational competency in
the future.
The operating infrastructure for building a smart maintenance factory is the power
supply system. Compared to a traditional machine-centered maintenance factory, a smart
maintenance factory emphasizes the importance of a stable power supply. Thus, it is es-
sential to build an ESS system to assure an affordable and stable power supply.
Moreover, environmental pollutants such as noise, dusts, gases, and hazardous oil
generated during the maintenance process were to be carried out in a separate and safe
workplace. Furthermore, a separate smart warehouse was built to store inventory and
material for maintenance in order to minimize wait time. For the purpose of promoting a
worker use environment with work safety and convenient information utilization, such
as PDA terminals, smart helmets, and wearables, the smart maintenance factory was con-
structed to associate with each technology and system.
(7) Analyze Effect with KPIs
It is proper to define KPIs based on the missions and operational values of a smart
maintenance factory. In this study, the analysis of an expected value requires an analysis
of the factory unit. In the case of a single operation, in order to improve the expected effect,
an excessive load or performance degradation of related operations may result, and a view
of managing the whole is necessary. Aligning KPIs with the expected effects is a plan for
operation optimization after the establishment of a smart maintenance factory by linking
strategy and implementation.
In connection with the goals of the vessel maintenance depot, the maintenance fac-
tory used mean time to failure (MTTF) and mean time to repair (MTTR) as KPIs to increase
the availability rate of vessels and improve the productivity of the maintenance factory.
Based on the two KPIs, the expected effect was calculated by improving the vessel avail-
ability rate and shortening the maintenance time through the establishment of a smart
maintenance factory. Through this, the alignment among the Goal-KPI-Expected effect
was secured.
While constructing a smart maintenance factory, the question about the benefits of
smart transformation has arisen. The Korean Ministry of SMEs and Startups analyzed the
effect of smart factory introduction for 5,003 SMEs from 2014 to 2017, and as a result,
productivity increased by 30% [54]. The report also provided the results of an application
system by companies. In this study, we set improvement goals for each stage and system,
based on the empirical productivity improvement effect and the MTTR and MTTF refer-
ence values managed internally.
Since this project was in the stage of designing smart transformation, the goal to be
obtained in the future was set rather than the actual application result. The goals for each
stage are shown in Table 2.
Machines 2021, 9, 267 16 of 20
Table 2. Goals of improvement.
In this project, when operating the smart maintenance factory for five years, the ex-
pected effect was estimated to be up to 30% for productivity improvement and up to 12%
for vessel operation rate improvement. The total cost of transforming the entire smart
maintenance factory was estimated to be about 30 million USD and the expected payback
period was three to five years (excluding equipment costs for infrastructure and automa-
(8) Plan Implementation
It took about one year to analyze the current status of the existing maintenance fac-
tories and establish a construction plan for smart transformation on factory basis, rather
than on the particular job or technology basis.
In this study, we transformed by resolving the issues presented in Table 1. The deport
of mechanical and hydraulic maintenance feature was transformed to a data-driven and
technology-driven maintenance factory. The first change was to establish a data-driven
management with data set definition, gathering, analysis, and standardization on the ba-
sis of big data governance. Second, QR, IoT (Bluetooth), and LTE-based networks enabled
data communication for the entire smart maintenance depot. Third, an on-site operation
support IT system was established to support workers, process, and field managers with
information and technical support. In particular, in order to secure the visibility of the site,
depot, 5M, and environmental status and information were centralized on the central con-
trol system. Fourth, a remote data collection system and AR technology were applied for
remote maintenance.
Fifth, semi-automation was focused on 3D printing/scanning, Auto Guided Vehicle,
and co-Robot to ensure the accuracy of work and the convenience of moving heavy equip-
ment. Finally, a smart warehouse, Energy Saving System, and a workshop for hazardous
environments were constructed separately for health and safety. Figure 9 illustrates the
changed image of the smart maintenance depot.
Transformation Contribution
MTTR Improvement MTTF Improvement
Year0 Year1 Year2 Year3 Year4 Year0 Year1 Year2 Year3 Year4
Big data 10% 0.0% 0.6% 1.5% 2.4% 3.0% 0.0% 0.2% 0.6% 1.0% 1.2%
IoT & Mobile 10% 0.0% 0.6% 1.5% 2.4% 3.0% 0.0% 0.2% 0.6% 1.0% 1.2%
OSS 25% 0.0% 1.5% 3.8% 6.0% 7.5% 0.0% 0.6% 1.5% 2.4% 3.0%
Remote Diagnostic 15% 0.0% 0.9% 2.3% 3.6% 4.5% 0.0% 0.4% 0.9% 1.4% 1.8%
Automation 20% 0.0% 1.2% 3.0% 4.8% 6.0% 0.0% 0.5% 1.2% 1.9% 2.4%
Infrastructure 20% 0.0% 1.2% 3.0% 4.8% 6.0% 0.0% 0.5% 1.2% 1.9% 2.4%
Total Improvement 100% 0% 6% 15% 24% 30% 0% 2% 6% 10% 12%
Machines 2021, 9, 267 17 of 20
Figure 9. Future image of a transformed smart depot.
The design and developed jobs of the proposed construction plan of the smart
maintenance factory will be promoted to ensure that the smart maintenance factory is to
be implemented step by step. The pilot working process (shop) was selected and deployed
all technologies that were under consideration and will evaluate the performance and de-
ploy the technologies to other work processes over the next five years.
5. Discussion and Conclusions
In terms of the entire lifecycle, smart factories have been mainly studied in the build-
ing phase, and the need to expand research to the service phase has been suggested
[6,7,32]. This study emphasized the service phase of the smart factory for a vessel mainte-
nance depot. Due to the variability of repair and maintenance work, there was a limit to
applying the smart factory concept in the manufacturing field as it is. The procedures and
methods were proposed for the smart transformation of maintenance factory by applying
Industrial 4.0 technology.
In order to respond to the variability of maintenance works, methods for securing
on-site agility and predicting maintenance were presented. For an agile response to the
field, we mainly set the direction of visualization on-site to control the processes in line
through the visual management and to secure the linkage between each job through the
on-site operation support system. For predictive maintenance, data of the status of the
currently operating vessels was collected in real-time and analyzed by the big data sys-
tem. The entire maintenance depot was built as a mobile workplace with IoT and LTE
enabling data-based factory management.
In data-driven factories, data standardization should be implemented through data
governance. Although the goal is to achieve efficient management with big data analysis,
the reality is that factories neither collect/manage data nor have a modeling concept for
the data. Since the IT system has stored data without considering the data format or type,
it has been difficult to utilize the stored data.
The technology-oriented automation was implemented rather than equipment-ori-
ented automation. For automation, semi-automation was proposed rather than full auto-
mation considering the work characteristics. Instead of the investment-oriented levels of
a smart factory, effect-driven automation should be given the top priority in consideration
of the difficulty of learning for operators and the work environment of the factory.
While IT innovation improves the process by applying the necessary technologies
and systems, smart transformation changes existing processes through the application of
the fourth industrial technologies of data, systems, automation, and infrastructure. In
other words, the approach of smart transformation is the opposite perspective to the leg-
acy IT approach.
Machines 2021, 9, 267 18 of 20
The concept of a smart factory in the ship building phase needed to be changed for
the application of the smart maintenance factory, but most of the technologies including
3D, AR, big data, IoT, and mobile could be accommodated. The applied enablers in the
study were IoT system, wireless system, operation support IT system, big data system,
remote support system, semi-automation, and infrastructure improvement. Among them,
the contribution to improvement was evaluated to be the highest through the operating
support system that supports the connection and visualization on-site.
Through the linkage between the mission, KPIs, and expected effect, the goals of
smart transformation for the plant rather than the individual work could be carried out.
This linkage is considered as a basic strategic direction, enabling the performance-ori-
ented smart maintenance factory operation.
In summary, we conducted a study to expand the smart factory concept, which was
limited to the ship building phase and to the ship servicing phase. The smart transfor-
mation procedure, framework, and architecture for a smart maintenance factory were pro-
posed with a practical case. The designed practical case was embodied as a data-driven
and technology-driven smart factory.
The results of this paper will be a reference model for building a smart maintenance
factory in the service area. Future studies should verify the actual increases of MTTFs and
reductions of MTTR through smart maintenance factory operation. The expansion and
improvement of the approach to business areas other than vessel maintenance could also
be studied further.
Author Contributions Conceptualization, methodology, G.S.K.; supervision, Y.H.L. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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The role of enterprise information systems is becoming increasingly crucial for improving customer responsiveness in the manufacturing industry. However, manufacturers engaged in mass customization are currently facing challenges related to implementing Industrial Internet of Things (IIoT) concepts of Industry 4.0 in order to increase responsiveness. In this article, we apply the findings from a two-year design science study to establish the role of manufacturing execution systems/manufacturing operations management (MES/MOM) in an IIoT-enabled brownfield manufacturing enterprise. We also present design recommendations for developing next-generation MES/MOM as a strong core to make factories smart and responsive. First, we analyze the architectural design challenges of MES/MOM in IIoT through a selective literature review. We then present an exploratory case study in which we implement our homegrown MES/MOM data model design based on ISA 95 in Aalborg University's Smart Production Lab, which is a reconfigurable cyber-physical production system. This was achieved through the use of a custom module for the open-source Odoo ERP platform (mainly version 14). Finally, we enrich our case study with three industrial design demonstrators and combine the findings with a quality function deployment (QFD) method to determine design requirements for next-generation IIoT-connected MES/MOM. The results from our QFD analysis indicate that interoperability is the most important characteristic when designing a responsive smart factory, with the highest relative importance of 31% of the eight characteristics we studied.
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Industry 4.0 is a tsunami that will invade the whole world. The real challenge of the future factories requires a high degree of reliability both in machinery and equipment. Thereupon, shifting the rudder towards new trends is an inevitable obligation in this fourth industrial revolution where the maintenance system has radically changed to a new one called predictive maintenance 4.0 (PdM 4.0). This latter is used to avoid predicted problems of machines and increase their lifespan taking into account that if machines have not any predicted problem, they will never be checked. However, in order to get successful prediction of any kind of problems, minimizing energy and resources consumption along with saving costs, this PdM 4.0 needs many new emerging technologies such as the internet of things infrastructure, collection and distribution of data from different smart sensors, analyzing/interpreting a huge amount of data using machine/deep learning…etc. This paper is devoted to present the industry 4.0 and its specific technologies used to ameliorate the existing predictive maintenance strategy. An example is given via a web platform to get a clear idea of how PdM 4.0 is applied in smart factories.
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With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.
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To make production more competitive, the new factories need to be built according to the guidelines of smart factories. The existing literature describes different approaches how to plan and design the smart factories. Therefore, the paper presents a step-by-step methodology for efficient planning of smart factory from the initial idea to the final realization in the real environment. The methodology includes eight crucial steps, which covers organizing the workshops in the company, explaining terms and definitions commonly used for smart factories, analysing and defining the employee's competences and the technological level of the company, analysing the possibilities of integration of smart I4.0 technologies, developing of digital twins at different levels, designing the visualization and finally building the smart factory in a real environment. The methodology is designed according to the needs of several partners and manufacturers, which are collaborating within the various European projects related to the smart factories.
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The main aim of this contribution is to outline the role and importance of key performance indicators in the frame of Industry 4.0 implementation. These key performance indicators are presented as a cornerstone for industry 4.0 implementation in organizational practice, since they represent key input for needed data in digitalized organization. In that framework, the contribution first exposes some of the essential characteristics of “Industry 4.0”, followed by the methodology of key performance indicators (KPI). Next, the contribution outlined a proposed methodology for implementing KPIs in frame of Industry 4.0 adoption in organizations. Another section of the paper is dedicatd to the linkage between corporate social responsilbty and KPIs in frame of Industry 4.0. The paper also outlines implications, limitations and further research directions are outlined.
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The implementation of Industry 4.0 and smart factory concepts changes the ways of manufacturing and production and requires the combination and interaction of different technologies and systems. The need for rapid implementation is steadily increasing as customers demand individualized products which are only possible if the production unit is smart and flexible. However, an existing factory cannot be transformed easily into a smart factory, especially not during operational mode. Therefore, designers and engineers require solutions which help to simulate the aspired change beforehand, thus running realistic pre-tests without disturbing operations and production. New product lines may also be tested beforehand. Data and the deduced knowledge are key factors of the said transformation. One idea for simulation is applying artificial intelligence, in this case the method of multi-agent-systems (MAS), to simulate the inter-dependencies of different production units based on individually configured orders. Once the smart factory is running additional machine learning methods for feedback data of the different machine units may be applied for generating knowledge for improvement of processes and decision making. This paper describes the necessary interaction of manufacturing and knowledge-based solutions before showing an MAS use case implementation of a production line using Anylogic.
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The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world. However, different terminologies, namely Intelligent Manufacturing (IM) and Smart Manufacturing (SM), have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners. While IM and SM are similar, they are not identical. From an evolutionary perspective, there has been little consideration on whether the definition, thought, connotation, and technical development of the concepts of SM or IM are consistent in the literature. To address this gap, the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of and clarify the relationship between IM and SM. A bibliometric analysis of publication sources, annual publication numbers, keyword frequency, and top regions of R&D establishes the scope and trends of the currently presented research. Critical topics discussed include origin, definitions, evolutionary path, and key technologies, of IM and SM. The implementation architecture, standards, and national focus are also discussed. In this work, a basis to understand SM and IM are provided, which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human-cyber-physical system (HCPS).
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Multi-material jetting (CerAM MMJ, previously T3DP) enables the additive manufacturing of ceramics, metals, glass and hardmetals, demonstrating comparatively high solid contents of the processed materials. The material is applied drop by drop onto a substrate. The droplets can be adapted to the component to be produced by a large degree of freedom in parameterization. Thus, large volumes can be processed quickly and fine structures can be displayed in detail, based on the droplet size. Data-driven methods are applied to build process knowledge and to contribute to the optimization of CerAM MMJ manufacturing processes. As a basis for the computational exploitation of mass sensor data from the technological process chain for manufacturing a component with CerAM MMJ, a data management plan was developed with the help of an engineering workflow. Focusing on the process step of green part production, droplet structures as intermediate products of 3D generation were described by means of droplet height, droplet circularity, the number of ambient satellite particles, as well as the associated standard deviations. First of all, the weighting of the factors influencing the droplet geometry was determined by means of single factor preliminary tests, in order to be able to reduce the number of factors to be considered in the detailed test series. The identification of key influences (falling time, needle lift, rising time, air supply pressure) permitted an optimization of the droplet geometry according to the introduced target characteristics by means of a design of experiments.
With the development of information & communication technology (ICT), industrial technology and management technology, manufacturing operation pattern and technology are improving quickly. In order to realize economic transformation and get their national competitiveness, American government proposed Re-industrialization and Industrial Internet, German government announced Industry 4.0, and Chinese government published Made in China 2025 national strategy. All of these mentioned strategies have a key topic: smart manufacturing. ISO, IEC, ITU, IEEE, and other international standard development organizations (SDOs) develop sets of international standards related to smart manufacturing. In order to present a systematic standardization solution for smart manufacturing, SDOs of the US, Germany, China and other countries developed their own national standards landscapes or roadmaps. In the paper, the new development of ICT and industrial technology are reviewed firstly. Then, these smart manufacturing architectures are analysed and compared. Thirdly, the reference model for smart manufacturing standards development and implementation is developed. At the end of the paper, a standards framework is provided.
Future industrial systems have been popularised in recent years through buzzwords such as Industry 4.0, the Internet of Things (IoT), and Cyber Physical Systems (CPS). Whilst the technologies of Industry 4.0 and likes have many conceivable benefits to manufacturing, the majority of these technologies are developed for, or by, large firms. Much of the contemporary work is therefore disconnected from the needs of small and medium-sized enterprises (SMEs), despite the fact they represent 90 % of registered companies in Europe. This study approaches the disconnect through an industrial survey of UK SMEs (n = 271, KMO = 0.701), which is the first in the UK that used to collect opinions, reinforcing the current literature on the most reported Industry 4.0 technologies (n = 20), benefits, and challenges to implementation. Flexibility, cost, efficiency, quality and competitive advantage are found to be the key benefits to Industry 4.0 adoption in SMEs. Whilst many SMEs show a desire to implement Industry 4.0 technologies for these reasons, financial and knowledge constraints are found to be key challenges.